Senior Research Associate

b Overview /b p In this role you will apply bioinformatic analyses to multi-omic datasets to identify biomarkers for hepatocellular carcinoma risk and treatment response. You will integrate clinical data within a secure research environment to support biomarker discovery, risk stratification, and early detection efforts as part of a multidisciplinary cancer research team. Your work will advance translational aims through data-driven insights and reproducible code, contributing to high-impact publications. /p b Pay / Benefits /b ul li generous holiday package /li li pension schemes /li li health and wellbeing initiatives /li li opportunity to buy additional holiday /li li competitive salary /li li career development opportunities /li /ul b Responsibilities /b ul li Lead bioinformatic analyses across SNPs, RNAseq, serum proteomics, WSI, and spatial data, integrating clinical information /li li Ensure safe, secure storage and deposition of large datasets in online repositories; support CRH-led data infrastructure /li li Maintain and contribute to REDCap database of clinical trial data for liver cancer treatments /li li Create, maintain and share version-controlled code/notebooks to enable reproducible analyses /li li Collect, analyze and interpret research data; draw conclusions to inform project objectives /li li Coordinate work with clinicians and researchers; solve problems to advance research aims /li li Disseminate findings through conferences and publications; contribute to project dissemination materials /li li Mentor junior colleagues; develop methodological and technical skills within the team /li li Define methodologies for research and identify opportunities for further investigations; contribute to grant applications /li li Lead ideas to enhance technical approaches and data analysis within the project /li /ul b Key requirements /b ul li Experience analyzing spatial and quantitative relationships in high-content multiplex histopathology images /li li Advanced statistical modelling of high-dimensional data; experience with penalised models and machine learning /li li Demonstrated understanding of RNAseq and gene expression analysis /li li Experience analyzing serum proteomic data (e.g., OLINK, SOMASCAN) /li li Experience handling securely large bioinformatic datasets /li li Knowledge of liver cancer staging, treatments, and predictive biomarkers /li li Attention to detail and high accuracy in analyses /li li Ability to present complex information clearly to diverse audiences /li li Proven data analysis and results reporting skills /li li Experience collaborating with colleagues and mentoring junior staff /li li Excellent IT skills across major office applications /li li Ability to work with initiative and creative problem solving /li li Understanding of the research environment and high analytical capacity /li li Ability to communicate complex information clearly and publish or present findings /li li Proven experience presenting at conferences or in publications /li /ul ul li collaboration /li li mentoring /li li communication /li li RNAseq analysis /li li RNA gene expression analysis /li li spatial data analyses /li /ul